SupplySage - The Intelligent Supply Orchestration Engine and Order Optimization System

1. Project Overview

A full-scale, AI-powered procurement management platform that streamlines vendor, RFQ, and purchase order management. It incorporates deep learning models for vendor scoring and price forecasting. Includes rich user interaction workflows with multi-role support (Admin, Procurement Manager, Finance Officer, Vendor).

2. Tech Stack

1. Backend API: Django + Django REST Framework
2. Deep Learning: PyTorch or TensorFlow (price prediction & vendor scoring)
3. Frontend UI: Django Templates or ReactJS
4. Database: PostgreSQL or SQLite
5. Authentication: Django Auth + Role Management
6. Deployment: Docker + Render/Heroku/AWS

3. Core Modules & Features

1. User Management with Roles
2. Vendor Onboarding and Evaluation
3. Quotation Requests and Comparison (RFQ)
4. Purchase Order Generation and Delivery Tracking
5. Deep Learning-powered Vendor Performance Classification
6. Dynamic Pricing Forecasting for Raw Materials
7. Role-based Dashboards and Alerts

4. User Roles & Interactions

1.Admin

1. Full access to system configuration, users, and ML monitoring
2. Manage user roles and system health

2.Procurement Manager

1. Create and issue RFQs
2. Review quotations and generate purchase orders
3. Access vendor ML scores and price predictions

3.Finance Officer

1. Approve or reject purchase orders
2. Compare cost estimates and select quotations

4.Vendor

1. Respond to RFQs
2. View and confirm purchase orders
3. Track delivery status and history

5. Machine Learning Features

5.A. Vendor Performance Scoring (Classification Model)

·- Input: delivery delay, defect rate, cost deviation, responsiveness

·- Model: Random Forest or Feedforward Neural Network

·- Output: Tiers (Excellent, Good, Average, Poor)

6.B. Dynamic Pricing Prediction (Regression/Sequence Model)

·- Input: material type, season, market trend, location

·- Model: LSTM or 1D CNN

·- Output: Price forecast for 30/60/90 days

6. Database Schema (Simplified)

1. UserProfile: user_id, role (admin, manager, finance, vendor)
2. Vendor: id, name, rating, contact, historical_score
3. RFQ: id, item, quantity, issued_by, status, deadline
4. Quotation: id, rfq (FK), vendor (FK), price, delivery_time
5. PurchaseOrder: id, vendor (FK), items, status, delivery_date
6. PriceForecast: id, material, forecast_date, predicted_price

7. Project Directory Structure

procurement_project/

├── manage.py

├── procurement/

│  ├── models.py

│  ├── views.py

│  ├── urls.py

│  ├── serializers.py

│  ├── templates/

│  ├── ml_model/

│  │├── vendor_classifier.py

│  │├── price_predictor.py

│  │└── model_files/

├── users/

│  └── user_profiles.py

├── static/

├── Dockerfile

└── requirements.txt

8. UI Interaction Workflows

1. Manager logs in → Creates RFQ → Vendor receives notification
2. Vendor logs in → Submits quotation → Finance compares prices
3. Manager approves vendor → Creates purchase order → Vendor confirms
4. ML model classifies vendor tier based on delivery history → Score displayed in UI
5. Price prediction auto-fills forecasted cost in quotation comparison form
6. Admin dashboard shows alerts: delayed delivery, price spikes, etc.

9. Deployment Plan

1. Dockerize Django app and ML components
2. Configure Gunicorn + Nginx for production
3. Use PostgreSQL in cloud deployment
4. Deploy to Render, Heroku, or AWS EC2
5. Schedule ML model updates via CRON or Celery

10. Suggested Development Timeline

1. Week 1: Setup project, models, user auth, and admin panel
2. Week 2: Implement vendor, RFQ, and quotation modules
3. Week 3: Build purchase order workflows and delivery tracking
4. Week 4: Train and integrate vendor scoring model
5. Week 5: Integrate price forecasting and dashboard
6. Week 6: Polish UI, add alerts, finalize deployment with Docker